Artificial Intelligence Based Identification and Monitoring of Student Using OpenCV

Nowadays, Artificial Intelligence plays a very crucial role in IT as well as Management sector. People are very busy and unproductive when it comes to handle huge terabytes of data. Tracking of candidate work in institution and organization can increase a productivity as well as the project completion ontime. One of the parts of tracking system we are introducing in picture is Attendance Monitoring and Identification of student or employee at the same time by using the OpenCV libraries. Ability to compute the attendance percentage as well as the introduce in the form of graphical representation is the most efficient way to come up. This project is nothing but the model or the software system which give solution related Attendance marking as well as admin of all the data related in the candidate to the most secure place and format. Here the GUI is the interface which is provided by developer to the user which having multiple option like to register and image recognition and model training and other admin power. Other than this looking to the task related to the data management and handling we use the database which is access by SQL queries. To retrieve and alter the relevant data in the database, queries are utilized. Using the model, views and controller in the system design, short form of the acronyms is MVC architecture. Application is installing in the organization as well as institution with designated candidate to handle it.

Authors

Institutions

Publication Details

Published
2024-09-27
DOI
https://doi.org/10.1109/acroset62108.2024.10743346
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial Intelligence Based Identification and Monitoring of Student Using OpenCV

Supreet Gaonkar, Mayank Mangal, Adarsh Pandey, Jayesh Wagh et al.
IoT-based Smart Home Systems
article

Artificial Intelligence Based Identification and Monitoring of Student Using OpenCV

Supreet Gaonkar, Mayank Mangal, Adarsh Pandey, Jayesh Wagh, Jagruti Bhanushali
article en

Abstract

Nowadays, Artificial Intelligence plays a very crucial role in IT as well as Management sector. People are very busy and unproductive when it comes to handle huge terabytes of data. Tracking of candidate work in institution and organization can increase a productivity as well as the project completion ontime. One of the parts of tracking system we are introducing in picture is Attendance Monitoring and Identification of student or employee at the same time by using the OpenCV libraries. Ability to compute the attendance percentage as well as the introduce in the form of graphical representation is the most efficient way to come up. This project is nothing but the model or the software system which give solution related Attendance marking as well as admin of all the data related in the candidate to the most secure place and format. Here the GUI is the interface which is provided by developer to the user which having multiple option like to register and image recognition and model training and other admin power. Other than this looking to the task related to the data management and handling we use the database which is access by SQL queries. To retrieve and alter the relevant data in the database, queries are utilized. Using the model, views and controller in the system design, short form of the acronyms is MVC architecture. Application is installing in the organization as well as institution with designated candidate to handle it.

University of Mumbai (IN)
Openalex Percentile: Top 82%
IoT-based Smart Home Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.